It seems that every week, AI technology has learned to do something humans do, but faster and better. From detecting cancers and eye conditions to predicting floods; or analyzing the language, tone, and facial expressions of candidates during recruitment processes, AI is now at the stage where it not only supports human judgment, but also makes increasingly more complex and accurate decisions.

As technology further improves and we learn how to better work and collaborate with AI, interactions between humans and computers will significantly enhance creativity—of both humans and bots.

Imagine that your boss Ethan calls you into his office. He expresses disappointment in your recent performance and lack of commitment. How would you react? Accept the feedback and put in more effort? Would you pout in your office and start looking for a new job?

Now, would your reaction be different if your boss was not named Ethan but Emily?

I’m a professor of economics, and my research investigates this very question. We hired 2,700 workers online to transcribe receipts, randomly assigning a male or female name to a manager, and randomly assigning which workers would receive performance feedback.

Results show that both women and men react more negatively to criticism if it comes from a woman. Our subjects reported that criticism by a woman led to a larger reduction in job satisfaction than criticism by a man. Employees were also doubly disinterested in working for the firm in the future if they had been criticized by a female boss.

Technology companies are frequently driven by their engineering processes. Of course product quality is regarded as most important, and that quality can be tested and measured with numbers and data. Such companies also frequently align their core identity with the engineering that belies their innovation. Their top executives often started out as engineers and keep looking primarily through their engineering lens as they become company leaders. Although it makes perfect sense, this approach is misguided.

One of the key ideas in lean manufacturing is that defects should be detected as early as possible. Efforts to control manufacturing processes, so that issues can be detected before defects occur, actually predate lean. Statistical process control (SPC) is a set of methods first created by Walter A. Shewhart at Bell Laboratories during the early 1920s. W. Edwards Deming standardized SPC for U.S. industry during WWII and introduced it to Japan during the American occupation after the war. SPC became a key part of Six Sigma, the Toyota Production System (TPS), and by extension, lean manufacturing.

SPC measures the outputs of processes, looking for small but statistically significant changes, so that corrections can be made before defects occur. SPC was first used within manufacturing, where it can greatly reduce waste due to rework and scrap. It can be used for any process that has a measurable output, and SPC is now widely used in service industries and healthcare.

How can industrial and manufacturing enterprises achieve better new product introduction (NPI), a critical element of operational excellence? Corporate goals of improving market share and revenue, maintaining competitive differentiators, and improving customer experiences are especially challenging when developing and launching new products—making it vitally important that NPI is seamless and high quality. Despite significant investment in NPI, a startling 44 percent of new products fail to meet most NPI success criteria.

Manufacturers and industrials face three key challenges:
• Organizational and data siloes, with little collaboration among increasingly complex supplier networks
• Core process deficiencies, as shown by key performance metrics, even as solutions are within reach
• Outdated or poorly integrated operations and quality systems, and data sources